Jason Hein received his B.Sc. in Biochemistry and Ph.D. in asymmetric reaction methodology from the University of Manitoba (NSERC PGS-A/B, Prof. Philip G. Hultin). He then became an NSERC postdoctoral research fellow with Prof. K. Barry Sharpless and Prof. Valery V. Fokin at the Scripps Research Institute in La Jolla, CA. In 2010, he became a senior research associate with Prof. Donna G. Blackmond at the Scripps Research Institute. He began his independent career at the University of California, Merced employing in-situ kinetic reaction analysis as a means to rapidlyprofile and study complex networks of reactions. In 2015, he moved to the University of British Columbia to continue the development of automated reaction analytical technology to serve mechanistic organic chemistry. Current studies are aimed at solving adiverse set of problems, including understanding catalytic reaction mechanisms, building robust chemical processes for manufacturing and developing coupled preferential crystallization technologies for chemical purification. These studies are enabled by a collection of prototype modular robotic tools and integrated analytical hardware which create the first broadly applicable automated reaction profiling toolkit.
Incorporating Computer Vision Into Crystallization Workflows
This presentation discusses the use of HeinSight (a modular vision package) to support the deployment of intelligent automation in chemical discovery.
Chemists spend an inordinate amount of time performing low-level tasks based on visual observation.For relatively routine synthetic organic workflow sequences, automation is beneficial. However, the full potential of integrated automated processes has proven challenging due to the dynamic and ever evolving environment of a chemical research lab.
In many cases, adding simple vision algorithms to control and monitor most of the processes in a standard synthetic lab can unlock the potential of automated workflows, and allow them to be rapidly and efficiently deployed to replace time and labor-intensive tasks. Furthermore, computer vision can play an even more impactful role when combined with advanced robotics. Image analysis algorithms can now transform simple digital cameras into powerful analytical tools for laboratory use.
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